feat(ai-210): detect coordinated abuse patterns across contributors, issues, and appeals#4
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khaadish wants to merge 7 commits into
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feat(ai-210): detect coordinated abuse patterns across contributors, issues, and appeals#4khaadish wants to merge 7 commits into
khaadish wants to merge 7 commits into
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- ScoreCalibrationService decouples raw model confidence from policy thresholds - CalibrationPolicy exposes approveThreshold, rejectThreshold, humanReviewThreshold, biasCorrectionFactor - needsHumanReview=true whenever confidence < humanReviewThreshold (explicit review boundary) - appliedPolicy + rawScore preserved in every output for audit/replay - Corresponding types exported from @devconsole/api-contracts - Spec covers approve/escalate/reject bands, bias correction, clamping, and traceability Closes Ibinola#423
… automation - ModelRolloutService supports pinned / canary / full rollout modes - rollback() restores previous RolloutConfig instantly (single-level undo) - resolveModel() is deterministic per requestId for consistent canary bucketing - setRollout / rollback emit structured log events for operator audit - RolloutConfig/RolloutResolution/ModelRolloutState types in @devconsole/api-contracts - Spec covers all three modes, determinism, rollback, and no-op safety Closes Ibinola#426
…issues, and appeals - CoordinatedAbuseDetectionService analyses batches of AbuseEvents for 4 pattern kinds - VELOCITY_CLUSTER, APPEAL_FLOODING, ISSUE_FARMING, SHARED_METADATA detectors - overallRisk and requiresHumanReview computed from _signalStrength (never automated punishment) - All thresholds in AbuseDetectionPolicy — tunable without code changes - _signalStrength internal; stripped from public API contract types - Structured coordinated_abuse_flagged log for operator pipelines - Spec covers all detectors, clean-events baseline, and immutability Closes Ibinola#427
…ation feat(ai-206): add policy-aware score calibration for AI appeal outputs
…rols feat(ai-209): add model rollback and safe rollout controls for appeal automation
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Summary
AI-assisted anomaly detection that helps operators spot coordinated rule-breaking, contributor farming, and suspicious appeal clusters — without automated punishment.
Changes
CoordinatedAbuseDetectionService— batch event analyser in thewavemoduleVELOCITY_CLUSTER,APPEAL_FLOODING,ISSUE_FARMING,SHARED_METADATACoordinatedAbuseReportwithpatterns[],overallRisk,requiresHumanReviewrequiresHumanReview=truefor any risk above low — no automated punishment path_signalStrengthdrives risk level but is stripped from public API typesAbuseDetectionPolicy— tunable without code changescoordinated_abuse_flaggedWARN log for operator pipelines@devconsole/api-contractsdocs/ai-210-coordinated-abuse-detection.mdAcceptance Criteria
AbuseEvent[],AbuseDetectionPolicy) and outputs (DetectedPattern[],overallRisk) — no hidden heuristicsAbuseDetectionPolicy— measurable and safe to tune over timerequiresHumanReviewis the only action gate; service never applies consequences directlyCloses Ibinola#427